Although we live in a world of constant motion, physicists have focused largely on systems in or near equilibrium. In the past few decades, interest in non-equilibrium systems has increased, spurred by developments that are taking quantum mechanics from fundamental science to practical technology. Physicists are therefore tasked with an important question: what organizing principles do non-equilibrium quantum systems obey? Herein Prüfer et al, Eigen et al, and Erne et al report experiments that provide a partial answer to this question. The studies show, for the first time, that ultracold atomic systems far from equilibrium exhibit universality, in which measurable experimental properties become independent of microscopic details. (MK) While the evolution of a many-body system is in general intractable in all its details, relevant observables can become insensitive to microscopic system parameters and initial conditions. This is the basis of the phenomenon of universality. Far from equilibrium universality is identified through the scaling of the spatio-temporal evolution of the system. This has been studied in the reheating process in inflationary cosmology, the dynamics of nuclear collision described by quantum chromodynamics, and the post-quench dynamics in dilute quantum gases in non-relativistic field theory. Here we observe the emergence of universal dynamics by spatially resolved spin correlations in a quasi-one-dimensional spinor Bose–Einstein condensate. (Prufer)

Understanding the behaviour of isolated quantum systems far from equilibrium and their equilibration is one of the most pressing problems in quantum many-body physics. There is strong theoretical evidence that sufficiently far from equilibrium a wide variety of systems — including the early Universe after inflation, quark–gluon matter generated in heavy-ion collisions, and cold quantum gases — exhibit universal scaling in time and space during their evolution, independent of their initial state or microscale properties. (Erne)

Kossio, Felipe, et al.
Growing Critical: Self-Organized Criticality in a Developing Neural System. arXiv:1811.02861.
As it becomes well known that brains seek and best perform in a state of mutual balance between more or less orderly complements, University of Bonn, Radboud University and King Juan Carlos University, Madrid neuroinformatic researchers describe experimental evidence that developmental brain maturations likewise proceed toward this optimum condition.

Experiments in various neural systems found avalanches: bursts of activity with characteristics typical for critical dynamics. A possible explanation for their occurrence is an underlying network that self-organizes into a critical state. We propose a simple spiking model for developing neural networks, showing how these may "grow into" criticality. Avalanches generated by our model correspond to clusters of widely applied Hawkes processes. We analytically derive the cluster size and duration distributions and find that they agree with those of experimentally observed neuronal avalanches. (Abstract)

Persi, Erez, et al.
Criticality in Tumor Evolution and Clinical Outcome. Proceedings of the National Academy of Sciences.
115/E11101,
2018.
University of Maryland and National Center for Biotechnology Information researchers including Yuri Wolf and Eugene Koonin report findings across a wide range of cancer cases that a complex generative dynamics is in effect which arrays as a critically poised state. It is said that appreciations of this common tendency could well aid diagnostics and treatment.

How mutation and selection determine the fitness landscape of tumors and hence clinical outcome is an open fundamental question in cancer biology, crucial for the assessment of therapeutic strategies and resistance to treatment. Here we explore the mutation-selection phase diagram of 6,721 tumors representing 23 cancer types by quantifying the overall somatic point mutation load (ML) and selection (dN/dS) in the entire proteome of each tumor. We show that ML strongly correlates with patient survival, revealing two opposing regimes around a critical point. In low-ML cancers, a high number of mutations indicates poor prognosis, whereas high-ML cancers show the opposite trend, presumably due to mutational meltdown. (Abstract excerpt)

Rocha, Rodrigo, et al.
Homeostatic Plasticity and Emergence of Functional Networks in a Whole-Brain Model at Criticality.Nature Scientific Reports.
8/15682,
2018.
After a decade of theory and test, University of Padova. Italy biophysicists including Samir Suweis and Amos Maritan contribute to current conclusions that dynamic cerebral network cognition does indeed reside in a self-organized, critically balanced state between control and creativity. See also Life at the Edge: Complexity and Criticality in Biological Function by Dante Chialvo at arXiv:1810.11737.

Understanding the relationship between large-scale structural and functional brain networks remains a crucial issue in modern neuroscience. Recently, there has been growing interest in investigating the role of homeostatic plasticity mechanisms in regulating network activity and brain functioning against a wide range of environmental conditions and brain states. In the present study, we investigate how the inclusion of homeostatic plasticity in a stochastic whole-brain model, implemented as a normalization of the incoming node’s excitatory input, affects the macroscopic activity during rest and the formation of functional networks. In this work, we show that normalization of the node’s excitatory input improves the correspondence between simulated neural patterns of the model and various brain functional data. Our results suggest that the inclusion of homeostatic principles lead to more realistic brain activity consistent with the hallmarks of criticality. (Abstract)

The emerging hypothesis is that living systems like the brain are spontaneously driven close to a critical phase transition thus conferring upon them the emergent features of critical systems. These characteristics would translate into the ability of the brain, through a large spatial and temporal scale activity, to promptly react to external stimuli by generating a coordinated global behavior, to maximize information transmission, sensitivity to sensory stimuli and storage of information. These ideas have been investigated in the last fifteen years in neuroscience and the hypothesis that the brain is poised near a critical state is gaining consensus. In brain systems, the concept of criticality is mainly supported by the following two experimental findings: the discovery of scale-free neural avalanches, as described by power-law distributions for the size and duration of the spontaneous bursts of activity in the cortex; and the presence of long-range temporal correlations in the amplitude fluctuations of neural oscillation. (2)

Bianconi, Ginestra.
Multilayer Networks: Structure and Function.
Oxford: Oxford University Press,
2018.
A Queen Mary University of London mathematician provides a comprehensive tutorial on these novel insights into how ubiquitous and deep nature’s organic and cerebral connectivities actually are. After a technical survey, it covers Communities, Centrality Measures, Interdependence, Epidemic Diffusion, and much more. See also Multiplex Networks: Basic Formalism and Structural Properties by Cozzo, Emanuele, et al (SpringerBriefs, 2018).

Multilayer networks is a rising topic in Network Science which characterizes the structure and the function of complex systems formed by several interacting networks. Multilayer networks research has been propelled forward by the wide realm of applications in social, biological and infrastructure networks and the large availability of network data, as well as by the significance of recent results, which have produced important advances. This book presents a comprehensive account of this emerging field by way of a theoretical and practical introduction to multilayer network science.

Ginestra Bianconi is Reader (Associate Professor) in Applied Mathematics and Director of the MSc in Network Science at the School of Mathematical Sciences, Queen Mary University of London. A physicist by training, since 2001 she has made network theory and its applications her central subject of investigation publishing more than one hundred papers. Currently her research focuses on multilayer networks, network geometry and percolation theory.

Cimini, Giulio, et al.
The Statistical Physics of Real-World Networks. arXiv:1810.05095.
In a paper to appear in the new Nature Reviews Physics (2019), IMT School for Advanced Studies, Lucca, Italy researchers including Guido Caldarelli expand appreciations of nature’s universal complex nodal and relational networks. A widely separate yet integral rooting of our global civilization into physical condensed matter can then be achieved. An illustration displays how the same multiplex phenomena arises from a independent source which is exemplified from agriculture and industry to travel and trade. See also The Dynamics of Knowledge Acquisition via Self-Learning in Complex Networks by this team at 1802.09337.

Statistical physics is the natural framework to model complex networks. In the last twenty years, it has brought novel physical insights on a variety of emergent phenomena, such as self-organization, scale invariance, mixed distributions and ensemble non-equivalence, which cannot be deduced from individual constituents, along with information theory and the principle of maximum entropy. We review the statistical physics approach for complex networks and the null models for the various physical problems, focusing on the analytic frameworks reproducing the local features of the network. We show how these models have been used to detect statistically significant and predictive structural patterns in real-world networks. We further survey the statistical physics frameworks that reproduce more complex, semi-local network features using Markov chain Monte Carlo sampling, and the models of generalised network structures such as multiplex networks, interacting networks and simplicial complexes. (Abstract edits)

Complex network theory has shown success in understanding the emergent and collective behavior of complex systems. Many real-world complex systems were recently discovered to be more accurately modeled as multiplex networks in which each interaction type is mapped to its own network layer such as transportation networks, coupled social networks, metabolic and regulatory networks, etc. A salient physical phenomena emerging from multiplexity is super-diffusion via an accelerated diffusion by the multi-layer structure as compared to any single layer. Here we show that modern machine (deep) learning, such as fully connected and convolutional neural networks, can classify and predict the presence of super-diffusion in multiplex networks. (Abstract excerpts)

Voitalov, ivan, et al.
Scale-free Networks Well Done. arXiv:1811:02071.
Northeastern University theorists including Dmitri Krioukov provide a further theoretical basis for the common, iterative presence of mathematical relation across all manner of natural and social networks.

We bring rigor to the vibrant activity of detecting power laws in empirical degree distributions in real-world networks. We first provide a definition of power-law distributions, equivalent to the definition of regularly varying distributions in statistics. This result allows the distribution to deviate from a pure power law arbitrarily but without affecting the power-law tail exponent. We identify three estimators of these exponents that are statistically consistent. Finally, we apply these estimators to a representative collection of synthetic and real-world data. (Abstract excerpt)

A power law is a relationship in which a relative change in one quantity gives rise to a proportional relative change in the other quantity, independent of the initial size of those quantities. (New England Complex Systems Institute)

Blount, Zachary, et al.
Contingency and Determinism in Evolution: Replaying Life’s Tape.Science.
362/655,
2018.
Some three decades ago Stephen Jay Gould claimed that in a bare environment of contingent selection only, sans any inherent source to guide life’s development, human-like sentient beings would not appear a second time. This extended paper by ZB and Richard Lenski, Michigan State University, and Jonathan Losos, Washington University, St. Louis (search each), which weaves results from past projects, implies that much evidence since bodes well for an opposite view. An historic shift toward a deep predictability accrues due to a consistent convergence across many lineages, which is notable in niche constructions, digital runs that produce these trends, and many anatomic and physiological cases. See for example How Fish Get Their Stripes Again and Again by Hugo Gante in Science (362/396, 2018) and Scalable Continuous Evolution of Genes at Mutation Rates above Genomic Error Thresholds by Arjun Ravikumar, et al at bioRxiv (May 3, 2018).

Historical processes display some degree of “contingency,” meaning their outcomes are sensitive to seemingly inconsequential events that can change the future. Unlike many other natural phenomena, evolution is a historical process. Evolutionary change is often driven by natural selection which works upon variation that arises by random mutation. Moreover, evolution has taken place within a planetary environment with a particular history of its own. Here we replicate populations in evolutionary “replay” experiments which often show parallel changes, especially in overall performance, although idiosyncratic outcomes can affect which of several evolutionary paths is taken. Comparative biologists have found many notable examples of convergent adaptation to similar conditions, but quantification of how frequently such convergence occurs is difficult. On balance, the evidence indicates that evolution tends to be surprisingly repeatable among closely related lineages, but disparate outcomes become more likely as the footprint of history grows deeper. (Abstract excerpts)

Erkurt, Murat.
Emergence of Form in Embryogenesis.Journal of the Royal Society Interface.
Vol.15/Iss.148,
2018.
After noting a long history from Aristotle’s preformations to 19th century recapitulations, an Imperial College London mathematician factors in Turing reaction-diffusion, epigenetic influences, gene regulatory networks and a need to recognize “self-organizing operators.” Into the 21st century, by these novel sapiensphere additions life’s evolutionary developmental gestation at term gains a definitive credence.

The development of form in an embryo is the result of a series of topological and informational symmetry breakings. We introduce the vector–reaction–diffusion–drift (VRDD) system where the limit cycle of spatial dynamics is morphogen concentrations with Dirac delta-type distributions. We developed ‘fundamental forms’ from spherical blastula with a single organizing axis (rotational symmetry), double axis (mirror symmetry) and triple axis (no symmetry operator in three dimensions). Using our integrated simulation model with four layers (topological, physical, chemical and regulatory), we generated life-like forms such as hydra. Genotype–phenotype mapping was investigated with continuous and jump mutations. Our study can have applications in morphogenetic engineering, soft robotics and biomimetic design. (Abstract excerpt)

In this paper, we introduced the VRDD system as a novel concept which can generate bodyplans of fundamental forms by self-organization. We then elaborated on an FSM (finite-state machine) model of the genetic regulatory network. The result of VRDD combined with the FSM model is spatial cell differentiation during embryogenesis that can be used for hierarchical modelling of complicated forms. We have demonstrated that our concepts are capable of generating self-organized bodyplans from which we developed life-like organism forms in silico. (10)

Schaerli, Yolanda, et al.
Synthetic Circuits Reveal how Mechanisms of Gene Regulatory Networks Constrain Evolution.Molecular Systems Biology.
14/9,
2018.
As the 21st century complexity revolution advances, biologists with postings in Switzerland, Spain and the UK including Andreas Wagner achieve novel insights into the naturally innate occasion of life’s oriented development. As the quotes allude, and other late entries agree, something more than particulate nucleotides alone must be going on. Discrete genes are actually nodal entities in pervasive network linkages, which altogether carries procreative information. While random mutations occur, equally present multiplex topologies, which spring from an independent source, guide and channel toward preferred forms and pathways. This expansion in biological thinking to witness both genetic molecules and active connections as they compose whole genomes is the essence of a genesis synthesis. See also Inference of Developmental Gene Regulatory Networks beyond Classical Systems by Selene Fernandez-Valverde, et al (search) for another appreciation of GRN complements.

Phenotypic variation is the raw material of adaptive Darwinian evolution. The phenotypic variation found in organismal development is biased towards certain phenotypes, but the molecular mechanisms are still poorly understood. Here we study evolutionary biases in two synthetic gene regulatory circuits expressed in Escherichia coli that produce a gene expression stripe—a pivotal pattern in embryonic development. The two parental circuits produce the same phenotype, but create it through different regulatory mechanisms. We show that mutations cause distinct novel phenotypes in the two networks and use a combination of experimental measurements, mathematical modelling and DNA sequencing to understand why mutations bring forth only some but not other novel gene expression phenotypes. Our results reveal that the regulatory mechanisms of networks restrict the possible phenotypic variation upon mutation. (Abstract)

A mathematical model describing the regulatory mechanisms of the two networks allowed us to understand the differences between accessible novel phenotypes for the two networks. The model predictions are also supported by DNA sequencing data. We thus provide for the first time empirical evidence that GRNs with different regulatory mechanisms can cause different constrained variation, (10)

Since the 19th century, Darwinian evolutionary biology has focused on natural selection and its power to shape populations and species. Natural selection, however, requires phenotypic variation, and the molecular mechanisms by which DNA mutations produce novel phenotypes have only become understood in recent years. While orthodox evolutionary theory assumed, often tacitly, that DNA mutations may produce any kind of variation, the discovery of constrained phenotypic variation challenged this view. As we show here, constrained variation in simple yet important spatial gene expression patterns can be explained by the simple fact that genes are embedded in regulatory networks. What is more, the regulatory mechanisms of these GRNs can help explain why specific gene expression patterns originate preferentially. Given the pervasive nonlinearity of gene regulatory networks, we surmise that constraints like those we observe are inherent in biological pattern‐forming systems. (12)

Ten Tusscher, Kirsten.
Of Mice and Plants: Comparative Developmental Systems Biology.Developmental Biology.
Online November,
2018.
While affinities between Metazoan fauna creatures are well proven, flora vegetation has not been similarly studied, or compared with animals. A Utrecht University computational developmental biologist here provides an initial survey of commonalities amongst plants and with regard to organisms. In collaboration with Paulien Hogeweg at UU and others, a case can be made because new biological systems and network organizations found across flora and fauna appear to exemplify the same structural source. The implication of independent, recurrent principles and process then becomes evident. See also In Silico Evo-Devo: Reconstructing Stages in the Evolution of Animal Segmentation by KtT, Renske Vroomans and Paulien Hogeweg in BMC EvoDevo (7/14, 2016).

Multicellular animals and plants represent independent evolutionary experiments with complex multicellular body plans. Differences in their life history, a mobile versus sessile lifestyle, and predominant embryonic vs. postembryonic development, have led to highly different anatomies. However, many intriguing parallels exist. Extension of the vertebrate body axis and its segmentation into somites has a striking resemblance to plant root growth and the prepatterning of lateral root competent sites. Likewise, plant shoot phyllotaxis displays is akin to vertebrate limb and digit patterning. Both plants and animals use complex signalling systems with systemic and local signals to fine tune and coordinate organ growth. Identification of these striking examples of convergent evolution provides support for the existence of general design principles: the idea that for particular patterning demands, evolution is likely to arrive at highly similar developmental patterning mechanisms. (Abstract excerpts)

Somites are body segments containing the same internal structures, clearly visible in invertebrates but also present in embryonic stages of vertebrates. Somites are transient, segmentally organized structures. In the vertebrate embryo, the somites contribute to multiple tissues, including the axial skeleton, skeletal and smooth muscles, dorsal dermis, tendons, ligaments, cartilage and adipose tissue. (Web definitions)

Faragalla, Kyrillos, et al.
From Gene List to Gene Network: Recognizing Functional Connections that Regulate Behavioral Traits.Journal of Experimental Zoology B.
Online November,
2018.
Western University, Ontario biologists in coauthor Graham Thompson’s group post a decisive review of the need to shift from a particulate nucleotide phase, which winds up with long tabulations, to equally real multiplex interrelations. The paper uniquely goes on to extend a “network ladder” of node first, interactions next onto protein, neuronal, social and ecosystem stages, which appear as emergent radiations of the same dynamic topology.

The study of social breeding systems is often gene focused, and the field of insect sociobiology has been successful at assimilating tools and techniques from molecular biology. One common output from sociogenomic studies is a gene list, which is readily generated from microarray, RNA sequencing, or other molecular screens. Gene lists, however, are limited because the tabular information does not explain how genes interact with each other, or how they change in real time circumstances. Here, we promote a view from molecular systems biology, where gene lists are converted into gene networks that better describe these functional connections that regulate behavioral traits. We argue that because network analyses are not restricted to “genes” as nodes, their implementation can connect multiple levels of biological organization into a single, progressively complex study system. (Abstract excerpt)

Peter, Isabelle and Eric Davidson.
Genomic Control Process: Development and Evolution.
Cambridge, MA: Academic Press,
2015.
A CalTech biology professor and the geneticist (1937-2015, search) who was the founding theorist of gene regulatory networks provide a consummate volume to date of this major expansion of active genetic phenomena.

Chapter 1 explains different levels of control affecting developmental gene expression in animal cells, and an overview of the physical nature of the regulatory genome. The book goes on to provide in depth understandings of GRNs, how they generate the regulatory conditions, cis-regulatory functions operating at the network nodes, and the dynamics of transcriptional activity in development. The next Chapters apply network theory to embryonic development of all major kinds; development of adult body parts and organs; and to cell fate specification. Chapter 6 examines the conceptual richness that has derived from various approaches to predictive, quantitative models of GRNs and GRN circuits. In The final section the notes applications to bilaterian evolution, including the underlying explanation of hierarchical animal phylogeny, and more. (Publisher excerpt)